Metricas_teste_wan

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1473
• Accuracy: 0.9818

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	248	0.3574	0.9114
No log	2.0	496	0.1911	0.9386
0.7732	3.0	744	0.1919	0.9386
0.7732	4.0	992	0.1044	0.9727
0.0987	5.0	1240	0.0928	0.9682
0.0987	6.0	1488	0.0545	0.9841
0.0406	7.0	1736	0.1183	0.9727
0.0406	8.0	1984	0.1114	0.9773
0.0204	9.0	2232	0.0838	0.9773
0.0204	10.0	2480	0.0726	0.9818
0.0084	11.0	2728	0.1100	0.975
0.0084	12.0	2976	0.1133	0.9773
0.0032	13.0	3224	0.1283	0.9773
0.0032	14.0	3472	0.0935	0.9795
0.0084	15.0	3720	0.1318	0.9705
0.0084	16.0	3968	0.1446	0.9773
0.0031	17.0	4216	0.1123	0.9773
0.0031	18.0	4464	0.0971	0.975
0.0049	19.0	4712	0.1369	0.9773
0.0049	20.0	4960	0.1855	0.9773
0.0018	21.0	5208	0.2224	0.9659
0.0018	22.0	5456	0.1444	0.9795
0.0045	23.0	5704	0.1544	0.9795
0.0045	24.0	5952	0.1495	0.9705
0.0037	25.0	6200	0.1741	0.975
0.0037	26.0	6448	0.1658	0.9705
0.0001	27.0	6696	0.2132	0.9727
0.0001	28.0	6944	0.2222	0.9682
0.0079	29.0	7192	0.1348	0.9795
0.0079	30.0	7440	0.1656	0.9773
0.0016	31.0	7688	0.1584	0.975
0.0016	32.0	7936	0.1674	0.9795
0.0005	33.0	8184	0.1837	0.9795
0.0005	34.0	8432	0.1595	0.9773
0.0016	35.0	8680	0.1949	0.9795
0.0016	36.0	8928	0.0991	0.9818
0.0004	37.0	9176	0.1864	0.9795
0.0004	38.0	9424	0.1444	0.9795
0.0024	39.0	9672	0.1486	0.9773
0.0024	40.0	9920	0.1457	0.9773
0.0004	41.0	10168	0.1486	0.9773
0.0004	42.0	10416	0.1518	0.9773
0.0	43.0	10664	0.1517	0.9773
0.0	44.0	10912	0.1480	0.9773
0.0	45.0	11160	0.1458	0.9818
0.0	46.0	11408	0.1462	0.9818
0.0	47.0	11656	0.1466	0.9818
0.0	48.0	11904	0.1470	0.9818
0.0	49.0	12152	0.1472	0.9818
0.0	50.0	12400	0.1473	0.9818

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.13.0
• Tokenizers 0.13.3